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<div class="section">
<div class="titlepage"><div><div>
<h2 class="title">
<a id="_cross_fields_queries"></a>cross-fields Queries<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/110_Multi_Field_Search/50_Cross_field.asciidoc">edit</a>
</h2>
</div></div></div>
<p>The custom <code class="literal">_all</code> approach is a good solution, as long as you thought
about setting it up before you indexed your documents. However, Elasticsearch
also provides a search-time solution to the problem: the <code class="literal">multi_match</code> query
with type <code class="literal">cross_fields</code>.
The <code class="literal">cross_fields</code> type takes a term-centric approach, quite different from the
field-centric approach taken by <code class="literal">best_fields</code> and <code class="literal">most_fields</code>. It treats all
of the fields as one big field, and looks for <em>each term</em> in <em>any field</em>.</p>
<p>To illustrate the difference between field-centric and term-centric queries,
look at the <code class="literal">explanation</code> for this field-centric <code class="literal">most_fields</code> query:</p>
<div class="pre_wrapper lang-sense">
<pre class="programlisting prettyprint lang-sense">GET /_validate/query?explain
{
    "query": {
        "multi_match": {
            "query":       "peter smith",
            "type":        "most_fields",
            "operator":    "and", <a id="CO78-1"></a><i class="conum" data-value="1"></i>
            "fields":      [ "first_name", "last_name" ]
        }
    }
}</pre>
</div>
<div class="sense_widget" data-snippet="snippets/110_Multi_Field_Search/50_Cross_field.json"></div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO78-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>All terms are required.</p>
</td>
</tr>
</table>
</div>
<p>For a document to match, both <code class="literal">peter</code> and <code class="literal">smith</code> must appear in the same
field, either the <code class="literal">first_name</code> field or the <code class="literal">last_name</code> field:</p>
<pre class="literallayout">(+first_name:peter +first_name:smith)
(+last_name:peter  +last_name:smith)</pre>

<p>A <em>term-centric</em> approach would use this logic instead:</p>
<pre class="literallayout">+(first_name:peter last_name:peter)
+(first_name:smith last_name:smith)</pre>

<p>In other words, the term <code class="literal">peter</code> must appear in either field, and the term
<code class="literal">smith</code> must appear in either field.</p>
<p>The <code class="literal">cross_fields</code> type first analyzes the query string to produce a list of
terms, and then it searches for each term in any field. That difference alone
solves two of the three problems that we listed in <a class="xref" href="field-centric.html" title="Field-Centric Queries">Field-Centric Queries</a>, leaving
us just with the issue of differing inverse document frequencies.</p>
<p>Fortunately, the <code class="literal">cross_fields</code> type solves this too, as can be seen from this
<code class="literal">validate-query</code> request:</p>
<div class="pre_wrapper lang-sense">
<pre class="programlisting prettyprint lang-sense">GET /_validate/query?explain
{
    "query": {
        "multi_match": {
            "query":       "peter smith",
            "type":        "cross_fields", <a id="CO79-1"></a><i class="conum" data-value="1"></i>
            "operator":    "and",
            "fields":      [ "first_name", "last_name" ]
        }
    }
}</pre>
</div>
<div class="sense_widget" data-snippet="snippets/110_Multi_Field_Search/50_Cross_field.json"></div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO79-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>Use <code class="literal">cross_fields</code> term-centric matching.</p>
</td>
</tr>
</table>
</div>
<p>It solves the term-frequency problem by <em>blending</em> inverse document
frequencies across fields: </p>
<pre class="literallayout">+blended("peter", fields: [first_name, last_name])
+blended("smith", fields: [first_name, last_name])</pre>

<p>In other words, it looks up the IDF of <code class="literal">smith</code> in both the <code class="literal">first_name</code> and
the <code class="literal">last_name</code> fields and uses the minimum of the two as the IDF for both
fields.  The fact that <code class="literal">smith</code> is a common last name means that it will be
treated as a common first name too.</p>
<div class="note admon">
<div class="icon"></div>
<div class="admon_content">
<p>For the <code class="literal">cross_fields</code> query type to work optimally, all fields should have
the same analyzer.  Fields that share an analyzer are grouped together as
blended fields.</p>
<p>If you include fields with a different analysis chain, they will be  added to
the query in the same way as for <code class="literal">best_fields</code>.  For instance, if we added the
<code class="literal">title</code> field to the preceding query (assuming it uses a different analyzer), the
explanation would be as follows:</p>
<pre class="literallayout">(+title:peter +title:smith)
(
  +blended("peter", fields: [first_name, last_name])
  +blended("smith", fields: [first_name, last_name])
)</pre>

<p>This is particularly important when using the <code class="literal">minimum_should_match</code> and
<code class="literal">operator</code> parameters.</p>
</div>
</div>
<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="_per_field_boosting"></a>Per-Field Boosting<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch-definitive-guide/edit/2.x/110_Multi_Field_Search/50_Cross_field.asciidoc">edit</a>
</h3>
</div></div></div>
<p>One of the advantages of using the <code class="literal">cross_fields</code> query over
<a class="xref" href="custom-all.html" title="Custom _all Fields">custom <code class="literal">_all</code> fields</a> is that you can boost individual
fields at query time.</p>
<p>For fields of equal value like <code class="literal">first_name</code> and <code class="literal">last_name</code>, this generally
isn’t required, but if you were searching for books using the <code class="literal">title</code> and
<code class="literal">description</code> fields, you might want to give more weight to the <code class="literal">title</code> field.
This can be done as described before with the caret (<code class="literal">^</code>) syntax:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">GET /books/_search
{
    "query": {
        "multi_match": {
            "query":       "peter smith",
            "type":        "cross_fields",
            "fields":      [ "title^2", "description" ] <a id="CO80-1"></a><i class="conum" data-value="1"></i>
        }
    }
}</pre>
</div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO80-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>The <code class="literal">title</code> field has a boost of <code class="literal">2</code>, while the <code class="literal">description</code> field
has the default boost of <code class="literal">1</code>.</p>
</td>
</tr>
</table>
</div>
<p>The advantage of being able to boost individual fields should be weighed
against the cost of querying multiple fields instead of querying a single
custom <code class="literal">_all</code> field. Use whichever of the two solutions that delivers the most
bang for your buck.</p>
</div>

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